
At first, we introduce another evaluation metric, score gap, which is the gap between the average scores of abnormal clips and normal clips. Larger score gap indicates [rgb]0,0.,0the network is more capable of distinguishing anomalies from [rgb]0.,0,0normal events [1]}.
[rgb]0,0.,0We conduct ablation studies on UCF-Crime to analyze the impact of generated pseudo labels (PLs), the self-guided attention module (SGA), and classifier head \(H_g\)  in SGA of proposed feature encoder \(E_{SGA}\)  in Table REF . Compared with the baseline and MIST\(^{\textup {w/o PLs}}\) , our MIST achievees a significant improvement when the generated pseudo labels are utilized. In particular, we observe 8.17% improvement in AUC and an approximately 17% score gap, which shows the efficacy of our multiple instance pseudo label generator with the sparse continuous sampling strategy. [rgb]0,0,0Pseudo labels also plays an important role. Compared with MIST, the performance of MIST\(^{\textup {w/o PLs}}\)  drops seriously, even worse than the baseline for the low-quality supervision that influences the attention map \(\mathcal {A}\)  generation from SGA.
